{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VelocityKernel vs. RealTimeKernel - TSI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Library imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import mplscience\n",
    "import seaborn as sns\n",
    "\n",
    "from cr2.analysis import plot_tsi\n",
    "\n",
    "sys.path.extend([\"../../../\", \".\"])\n",
    "from paths import DATA_DIR, FIG_DIR  # isort: skip  # noqa: E402"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAVE_FIGURES = False\n",
    "if SAVE_FIGURES:\n",
    "    (FIG_DIR / \"realtime_kernel\" / \"pharyngeal_endoderm\").mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "FIGURE_FORMAT = \"pdf\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_macrostates</th>\n",
       "      <th>identified_terminal_states</th>\n",
       "      <th>optimal_identification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_macrostates  identified_terminal_states  optimal_identification\n",
       "0                     20                           4                      11\n",
       "1                     19                           4                      11\n",
       "2                     18                           4                      11\n",
       "3                     17                           4                      11\n",
       "4                     16                           4                      11"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsi_cr1_full = pd.read_csv(DATA_DIR / \"pharyngeal_endoderm\" / \"results\" / \"tsi-full_data-vk.csv\")\n",
    "tsi_cr1_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_macrostates</th>\n",
       "      <th>identified_terminal_states</th>\n",
       "      <th>optimal_identification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_macrostates  identified_terminal_states  optimal_identification\n",
       "0                     10                           4                       4\n",
       "1                      9                           4                       4\n",
       "2                      8                           4                       4\n",
       "3                      7                           4                       4\n",
       "4                      6                           4                       4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsi_cr1_subset = pd.read_csv(DATA_DIR / \"pharyngeal_endoderm\" / \"results\" / \"tsi-subsetted_data-vk.csv\")\n",
    "tsi_cr1_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_macrostates</th>\n",
       "      <th>identified_terminal_states</th>\n",
       "      <th>optimal_identification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_macrostates  identified_terminal_states  optimal_identification\n",
       "0                      1                           1                       1\n",
       "1                      2                           2                       2\n",
       "2                      3                           3                       3\n",
       "3                      4                           4                       4\n",
       "4                      5                           5                       5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsi_cr2_full = pd.read_csv(DATA_DIR / \"pharyngeal_endoderm\" / \"results\" / \"tsi-full_data-rtk.csv\")\n",
    "tsi_cr2_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_macrostates</th>\n",
       "      <th>identified_terminal_states</th>\n",
       "      <th>optimal_identification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_macrostates  identified_terminal_states  optimal_identification\n",
       "0                     10                           4                       4\n",
       "1                      9                           4                       4\n",
       "2                      8                           4                       4\n",
       "3                      7                           4                       4\n",
       "4                      6                           4                       4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsi_cr2_subset = pd.read_csv(DATA_DIR / \"pharyngeal_endoderm\" / \"results\" / \"tsi-subsetted_data-rtk.csv\")\n",
    "tsi_cr2_subset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsi_cr1_full[\"method\"] = \"CellRank 1\"\n",
    "tsi_cr1_subset[\"method\"] = \"CellRank 1\"\n",
    "\n",
    "tsi_cr2_full[\"method\"] = \"CellRank 2\"\n",
    "tsi_cr2_subset[\"method\"] = \"CellRank 2\"\n",
    "\n",
    "df_full = pd.concat([tsi_cr1_full, tsi_cr2_full])\n",
    "df_subset = pd.concat([tsi_cr1_subset, tsi_cr2_subset])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = {\"CellRank 1\": \"#0173b2\", \"CellRank 2\": \"#DE8F05\", \"Optimal identification\": \"#000000\"}\n",
    "\n",
    "if SAVE_FIGURES:\n",
    "    fname = FIG_DIR / \"realtime_kernel\" / \"pharyngeal_endoderm\" / f\"tsi_ranking-full_data.{FIGURE_FORMAT}\"\n",
    "else:\n",
    "    fname = None\n",
    "\n",
    "with mplscience.style_context():\n",
    "    sns.set_style(style=\"whitegrid\")\n",
    "    plot_tsi(df=df_full, palette=palette, fname=fname)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = {\"CellRank 1\": \"#0173b2\", \"CellRank 2\": \"#DE8F05\", \"Optimal identification\": \"#000000\"}\n",
    "\n",
    "if SAVE_FIGURES:\n",
    "    fname = FIG_DIR / \"realtime_kernel\" / \"pharyngeal_endoderm\" / f\"tsi_ranking-subsetted_data.{FIGURE_FORMAT}\"\n",
    "else:\n",
    "    fname = None\n",
    "\n",
    "with mplscience.style_context():\n",
    "    sns.set_style(style=\"whitegrid\")\n",
    "    plot_tsi(df=df_subset, palette=palette, fname=fname)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cr2-py38",
   "language": "python",
   "name": "cr2-py38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}